class_docstr: Bridge class to connect langchain.embeddings module to mindsDB
class_docstr_tok: ["Bridge","class","to","connect","langchain.embeddings","module","to","mindsDB"]
code: def describe(self, attribute: Union[str, None] = None) -> DataFrame:
      args = self.model_storage.json_get("args")
  
      if attribute == "args":
          return pd.DataFrame(args.items(), columns=["key", "value"])
      elif attribute == "metadata":
          return pd.DataFrame(
              [
                  (
                      "model_class",
                      self.model_storage.json_get("model_class"),
                  ),
              ],
              columns=["key", "value"],
          )
  
      else:
          tables = ("args", "metadata")
          return pd.DataFrame(tables, columns=["tables"])
  
file_path: mindsdb/mindsdb/integrations/handlers/langchain_embedding_handler/langchain_embedding_handler.py
imports: import copy
  import importlib
  from typing import Dict, Union
  import pandas
  from langchain.embeddings.base import Embeddings
  from pandas import DataFrame
  from mindsdb.integrations.libs.base import BaseMLEngine
  from mindsdb.utilities import log
label_desc: Use image node_id 6 for calling the LangchainEmbeddingHandler obj's underlying member method code with example usage: obj.describe(attribute) and returns: pd, pd, pd